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Do the AUC and log-loss evaluate CTR prediction models properly?
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- KATAGIRI Satoshi
- F@N Communications, Inc.
Bibliographic Information
- Other Title
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- CTR 予測モデルの評価に AUC や log-loss は適切か?
Description
<p>Click-through rate (CTR) prediction is one of the most important task for web advertising platform companies. However, CTR prediction is a non-standard machine learning task, so conventional metrics, for example, area under the Receiver Operating Characteristic curve (AUC), and log-loss, a.k.a. cross-entropy, and so on, can be improper. Our target is develop a new metrices for CTR prediction. In this article, we state the drawbacks of such conventional metrics and perspective of a metric based on the calibration plot approach.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2019 (0), 3K3J203-3K3J203, 2019
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390845713072711424
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- NII Article ID
- 130007658628
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed
